An Obstacle Avoidance Scheme for Manipulators Aided by Noise-Tolerant Neural Dynamics

There may be obstacles in the workspace of redundant manipulators, which generally pose a hidden danger to the safety execution. How to avoid obstacles reasonably is one of the goals of this article. To this end, a modified obstacle avoidance (MOA) method is proposed, which creates a larger feasible space for the escape velocity of redundant manipulators in a more concise form than the existing methods. Equipped with the MOA method, a trajectory-tracking and modified-obstacle-avoidance (TT-MOA) scheme for redundant manipulators is constructed. On the other hand, ubiquitous noises also influence the operation of redundant manipulators. Therefore, a noise-tolerant gradient neural dynamics (NTGND) model is proposed to tolerate noises when solving the TT-MOA scheme. Rigorous theoretical analyses prove the convergence and robustness of the NTGND model, and computer simulations and physical experiments demonstrate the practicability and superiority of the proposed methods compared with the existing techniques.